Next-basket recommendation (NBR) is a type of recommendation that aims to predict a set of items a user will purchase based on their historical transaction basket sequences. It is governed by a dynamic interplay between two distinct user intents: habitual repurchase, which involves repeating past behaviors, and exploratory interest, which involves discovering new items. However, existing NBR methods generally suffer from two limitations: (1) they often entangle these conflicting motives within a single representation, causing habits to overshadow discovery, and (2) they rely on discrete sequential modeling that ignores continuous-time intervals and item-specific periodicities. In this paper, we propose a novel solution named Time-Interval Disentangled Experts (TIDE) to address these challenges. TIDE incorporates a Hawkes-enhanced Fourier Time Encoding to capture item-specific temporal periodicities and dynamic decay. To decouple user intentions, TIDE utilizes a dual-expert architecture that integrates a Habit Expert for recurring needs and a Pattern-Guided Exploration Expert for discovery. Combined with an item-aware gating mechanism, TIDE adaptively balances repurchase and exploration. Extensive experiments on four diverse real-world datasets demonstrate that TIDE consistently outperforms representative state-of-the-art NBR methods.

Knowledge Graphs (KGs) have proven highly effective for recommendation systems by capturing latent item relationships, while recent integration of Large Language Models (LLMs) has further enhanced semantic understanding and addressed knowledge sparsity issues. Nevertheless, current KG&LLM-based methods still face three main limitations: 1) inadequate modeling of implicit semantics relationships beyond explicit KG links; 2) suboptimal single-channel fusion of ID and LLM embeddings, which often leads to signal interference and blurred representations; and 3) insufficient consideration of user-item interaction frequency variations in recommendation strategies. To address these challenges, we propose the Dual-Channel Graph Learning (DCGL) framework, featuring three key innovations: 1) a dual-channel architecture that structurally decoupling rich semantic information from user behavioral patterns, preventing early interference; 2) a multi-level contrastive learning mechanism that enhances robustness against KG noise through intra-view contrast and bridges semantic gaps between channels via inter-view alignment; and 3) a dynamic fusion mechanism that adaptively balances semantic generalization and behavioral specificity based on interaction frequency, resolving the cascading limitation. Extensive experiments on four real-world datasets show that DCGL consistently outperforms state-of-the-art methods, yielding substantial improvements in sparse scenarios while maintaining precision for active users. Our code is available at https://github.com/XinchiZou/DCGL.

Sequential basket analysis is a challenging task that focuses on modeling user interests through their shopping basket records. This study focuses on a newly identified bias: the repetition bias , which typically arises due to repurchase behavior . Existing methods typically oversimplify the relationship between repetitions and predictions. They assume that frequent repetition of an item by a user indicates a strong preference of the user. However, this assumption is flawed as it fails to consider that repetitions are not driven solely by user interests, as they can also be influenced by external factors, resulting in a biased understanding of user interests. In this article, we propose the CA usal intervention for R epetition D e-biasing ( CARD ), a novel solution to comprehensively estimate various influencing factors and address the repetition bias, thereby ensuring a more accurate learning of user interests. Specifically, we design a multi-factor estimation debiasing framework with constructed causal graphs to formalize the data generation process within the recommendation. We then analyze the variables that influence the recommendation, with the goal of identifying confounding variables that affect repurchase behavior and thereby locating the source of repetition bias. Since repetition bias originates from the influence of confounding variables on repurchase behavior, we resort to causal intervention methods to prevent its impacts and thus eliminate repetition bias at its source for unbiased user interest modeling. We evaluate CARD by conducting extensive experiments over three real-world datasets. The results demonstrate our approach’s competitiveness over the representative state-of-the-art baselines in achieving unbiased user interest modeling.

Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE.

Session-based recommendation (SBR) aims to predict the anonymous user's next-click items by modeling the short-term sequence pattern. As most existing SBR models generally generate item representations based only on information propagation over the short sequence while ignoring additional valuable knowledge, their expressive abilities are somewhat limited by data sparsity caused by short sequence. Though there have been some attempts on utilizing items' attributes, they basically embed attributes into items directly, ignoring the fact that 1) there is no contextual relationship among attributes; and 2) users have varying levels of attention to different attributes, which still leads to unsatisfactory performance. To tackle the issues, we propose a novel Attribute-enhanced Dual Channel Representation Learning (ADRL) model for SBR, in which we independently model session representations in attribute-related pattern and sequence-related pattern. Specifically, we learn session representations with sequence patterns from the session graph, and we further design an frequency-driven attribute aggregator to generate the attribute-related session representations within a session. The proposed attribute aggregator is plug-and-play, as it can be coupled with most existing SBR models. Extensive experiments on three real-world public datasets demonstrate the superiority of the proposed ADRL over several state-of-the-art baselines, as well as the effectiveness and efficiency of our attribute aggregator module.

Session-based recommendation (SBR) has received increasing attention to predict the next item via extracting and integrating both global and local item-item relationships. However, there still exist some deficiencies in current works when capturing these two kinds of relationships. For global item-item relationships, the global graph constructed by most SBR is a pseudo-global graph, which may cause redundant mining of sequence relationships. For local item-item relationships, conventional SBR only mines the sequence patterns while ignoring the feature patterns, which may introduce noise when learning users' interests. To address these problems, we propose a novel Knowledge-enhanced Multi-View Graph Neural Network (KMVG) by constructing three views, namely knowledge view, session view, and pairwise view. Specifically, benefiting from the rich semantic information in the knowledge graph (KG), we build a genuine global graph that is sequence-independent based on KG to mine the global item-item relationships in the knowledge view. Then, a session view is utilized to capture the contextual transitions among items as the sequence patterns of local item-item relationships, and a pairwise view is used to explore the feature commonality within a session as the feature patterns of the local item-item relationships. Extensive experiments on three real-world public datasets demonstrate the superiority of KMVG, showing that it outperforms the state-of-the-art baselines. Further analysis also reveals the effectiveness of KMVG in exploiting the item-item relationships under multiple views.

Next-basket recommendation (NBR) is a type of recommendation that aims to recommend a set of items to users according to their historical basket sequences. Existing NBR methods suffer from two limitations: (1) overlooking low-level item correlations, which results in coarse-grained item representation; and (2) failing to consider spurious interests in repeated behaviors, leading to suboptimal user interest learning. To address these limitations, we propose a novel solution named Multi-view Multi-aspect Neural Recommendation (MMNR) for NBR, which first normalizes the interactions from both the user-side and item-side, respectively, aiming to remove the spurious interests, and utilizes them as weights for items from different views to construct differentiated representations for each interaction item, enabling comprehensive user interest learning. Then, to capture low-level item correlations, MMNR models different aspects of items to obtain disentangled representations of items, thereby fully capturing multiple user interests. Extensive experiments on real-world datasets demonstrate the effectiveness of MMNR, showing that it consistently outperforms several state-of-the-art NBR methods.

Due to the success of graph convolutional networks (GCNs) in effectively extracting features in non-Euclidean spaces, GCNs has become the rising star in implicit collaborative filtering. Existing works, while encouraging, typically adopt simple aggregation operation on the user-item bipartite graph to model user and item representations, but neglect to mine the sufficient dependencies between nodes, e.g., the relationships between users/items and their neighbors (or congeners), resulting in inadequate graph representation learning. To address these problems, we propose a novel Multi-Dependency Graph Collaborative Filtering (MDGCF) model, which mines the neighborhood- and homogeneous-level dependencies to enhance the representation power of graph-based CF models. Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features. Besides, by adaptively mining the homogeneous-level dependencies among users and items, we construct two homogeneous graphs, based on which we further aggregate features from homogeneous users and items to supplement their representations, respectively. Extensive experiments on three real-world benchmark datasets demonstrate the effectiveness of the proposed MDGCF. Further experiments reveal that our model can capture rich dependencies between nodes for explaining user behaviors.

Reinforcement learning-based portfolio management has recently attracted extensive attention. However, deep reinforcement learning methods are unexplainable and considered to be potentially risky, difficult to be trusted and regulated by users. To address these problems, we propose an eXplainable reinforcement learning framework for Portfolio Management, named XPM, which is efficient, concise, and can provide faithful explanations for network outputs. Specifically, we first design a policy network for portfolio management, which uses temporal convolutional network (TCN) to extract temporal features of multiple time series in portfolio. Then, we employ global average pooling (GAP) and a fully connected layer to integrate the global feature maps to handle asset correlations. Finally, we utilize softmax to determine the output portfolio weights. To assemble explainability into our model, we employ an explainable artificial intelligence method, class activation mapping (CAM), to explain the network outputs, which computes an activation map for an asset of interest. The map highlights the important assets and time intervals in the input state. In this way, end users can understand which part of the portfolio's recent price movements makes the network decision to invest in the target asset. Experimental results show that XPM outperforms the current state-of-the-art portfolio management methods in NASDAQ and NYSE markets, and can provide faithful and informative explanations to end users.

Data sparsity is a challenge problem that most modern recommender systems are confronted with. By leveraging the knowledge from relevant domains, the cross-domain recommendation technique can be an effective way of alleviating the data sparsity problem. In this paper, we propose a novel Bi-directional Transfer learning method for cross-domain recommendation by using Graph Collaborative Filtering network as the base model (BiTGCF). BiTGCF not only exploits the high-order connectivity in user-item graph of single domain through a novel feature propagation layer, but also realizes the two-way transfer of knowledge across two domains by using the common user as the bridge. Moreover, distinct from previous cross-domain collaborative filtering methods, BiTGCF fuses users' common features and domain-specific features during transfer. Experimental results on four couple benchmark datasets verify the effectiveness of BiTGCF over state-of-the-art models in terms of bi-directional cross domain recommendation.

Financial portfolio management is the process of periodically reallocating a fund into different financial investment products, with the goal of achieving the maximum profits. While conventional financial machine learning methods try to predict the price trends, reinforcement learning based portfolio management methods makes trading decisions according to the price changes directly. However, existing reinforcement learning based methods are limited in extracting the price change information at single-scale level, which makes their performance still not satisfactory. In this paper, inspired by the Inception network that has achieved great success in computer vision and can extract multi-scale features simultaneously, we propose a novel Ensemble of Identical Independent Inception (EI$^3$) convolutional neural network, with the objective of addressing the limitation of existing reinforcement learning based portfolio management methods. With EI$^3$, multiple assets can be processed independently while sharing the same network parameters. Moreover, price movement information for each product can be extracted at multiple scales via wide network and then aggregated to make trading decision. Based on EI$^3$, we further propose a recurrent reinforcement learning framework to provide a deep machine learning solution for the portfolio management problem. Comprehensive experiments on the cryptocurrency datasets demonstrate the superiority of our method over existing competitors, in both upswing and downswing environments.